Dynamic interactions among large sets of brain regions produce all human perception, cognition and behavior. It is increasingly recognized that most mental disorders are caused by disruptions of distributed neural circuits, the structure and function of which still remain poorly known. Therefore, mapping the anatomy and dynamics of human brain networks is critical for us to understand the mechanisms underlying a variety of human behaviors and mental illness. However, significant progress in this area is hindered by technical limitations of existing neural recording and imaging techniques. To date, there is no single non-invasive neuroimaging technique ca- pable of providing a complete spatiotemporal pattern of whole-brain neuronal interactions. There is a critical need to establish new non-invasive imaging methods with high spatial and temporal resolution to uncover neural circuit dynamics in normal vs. diseased brains. To meet this critical need, we propose to establish and validate a novel multimodal hyperspectral imaging (MHI) technique, based on simultaneous acquisition and joint analysis of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), to permit high- resolution mapping of brain activity and connectivity at specific frequencies over the full spectrum of brain dynamics. This unique technique combined with diffusion MRI (dMRI) will be immediately usable to create a significantly enriched human brain connectome that will not only depict detailed connections among anatomically specific brain regions, but also assign to each region and each connection color-coded spectral signatures indicating their differential degrees of involvement in distributed network activities over various neuronal time scales across whole-brain neural circuits. To achieve this objective, we propose to accomplish three specific aims. 1) We will develop and optimize MHI through realistic computation simulations based on the virtual brain (TVB), a neuroinformatic platform to simulate the whole-brain network dynamics. 2) We will combine MHI and dMRI tractography to create a spectrally color-coded human connectome that entails both structural and functional connectivity. 3) We will validate the cortical activity and connectivity imaged with MHI against those directly measured with electrocorticography (ECoG) from the same group of epilepsy patients undergoing neuro- surgical evaluation with implanted subdural grids. The outcome from the proposed research will provide a new imaging tool to uncover the network basis for accurately assessing brain functions and identifying biomarkers for diagnosis of mental disorders. This project will have a significantly positive impact in delineating the brain's structural and functional connectivity, paving the way for better understanding and diagnosis of mental health, and significantly aid treatment and prevention of mental disorders.